# Explainable-AI powered stock price prediction using time series transformers: A Case Study on BIST100

arXiv: https://arxiv.org/abs/2506.06345 · HTML: https://mlsanity.com/paper/2506.06345/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 1 Jun 2025.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks](https://mlsanity.com/paper/1703.07015/) (2017)
- [A Transformer-based Framework for Multivariate Time Series Representation Learning](https://mlsanity.com/paper/2010.02803/) (2020)
- [Global Stock Market Prediction Based on Stock Chart Images Using Deep Q-Network](https://mlsanity.com/paper/1902.10948/) (2019)
- [Towards Responsible AI in Banking: Addressing Bias for Fair Decision-Making](https://mlsanity.com/paper/2401.08691/) (2024)
- [Impact of Financial Literacy on Investment Decisions and Stock Market Participation using Extreme Learning Machines](https://mlsanity.com/paper/2407.03498/) (2024)

## Built on by (most cited first, 1 of 1)

- [Empowering VLMs for Few-Shot Multimodal Time Series Classification via Tailored Agentic Reasoning](https://mlsanity.com/paper/2605.09395/) (2026)

## Abstract (arXiv)

Financial literacy is increasingly dependent on the ability to interpret complex financial data and utilize advanced forecasting tools. In this context, this study proposes a novel approach that combines transformer-based time series models with explainable artificial intelligence (XAI) to enhance the interpretability and accuracy of stock price predictions. The analysis focuses on the daily stock prices of the five highest-volume banks listed in the BIST100 index, along with XBANK and XU100 indices, covering the period from January 2015 to March 2025. Models including DLinear, LTSNet, Vanilla Transformer, and Time Series Transformer are employed, with input features enriched by technical indicators. SHAP and LIME techniques are used to provide transparency into the influence of individual features on model outputs. The results demonstrate the strong predictive capabilities of transformer models and highlight the potential of interpretable machine learning to empower individuals in making informed investment decisions and actively engaging in financial markets.